Triple
T17573026
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Airbus A320neo |
E427985
|
entity |
| Predicate | fuelEfficiencyImprovementOverA320ceo |
P41013
|
FINISHED |
| Object | approximately 15 percent |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: approximately 15 percent | Statement: [Airbus A320neo, fuelEfficiencyImprovementOverA320ceo, approximately 15 percent]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fuelEfficiencyImprovementOverA320ceo Context triple: [Airbus A320neo, fuelEfficiencyImprovementOverA320ceo, approximately 15 percent]
-
A.
typicalEfficiencyComparedToPredecessor
chosen
Indicates how the usual or average efficiency of something compares to that of its predecessor.
-
B.
fuelEfficiency
Indicates how effectively an entity uses fuel to perform a given amount of work or travel a certain distance.
-
C.
fuelEffect
Indicates the influence or impact that a given fuel has on a process, system, or outcome.
-
D.
aircraftModification
Indicates a relationship where an aircraft undergoes a change, upgrade, or alteration to its structure, systems, or configuration.
-
E.
ledToAircraftRedesign
Indicates that one event, issue, or discovery caused or motivated a change in the design of an aircraft.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69d889e0385081908a04b66f4dd4bd0d |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e459330c788190907a02fc98e0e24b |
completed | April 19, 2026, 4:25 a.m. |
| PD | Predicate disambiguation | batch_69e3b4fd7d048190b54ee4c6155612a5 |
completed | April 18, 2026, 4:44 p.m. |
Created at: April 10, 2026, 5:50 a.m.